LEAD DATA SCIENTIST

Role Purpose

Lead the design, engineering, and operationalization of enterprise Security Knowledge Graphs that connect security telemetry, assets, identities, vulnerabilities, threats, controls, and incidents into a trusted intelligence layer. The role is highly hands-on and combines data science, graph engineering, cybersecurity analytics, semantic modeling, and technical leadership to enable attack-path analysis, threat investigation, exposure prioritization, GraphRAG, and AI-assisted security operations. Key Responsibilities

• Design the Security Knowledge Graph architecture, ontology, taxonomy, entity model, relationship model, provenance model, and lifecycle standards. • Build production-grade graph ingestion and transformation pipelines for SIEM, EDR/XDR, IAM/PAM, CMDB, vulnerability scanners, cloud security platforms, threat intelligence feeds, security data lakes, and case-management systems. • Develop entity extraction, identity resolution, deduplication, schema mapping, relationship inference, confidence scoring, temporal modeling, and graph enrichment capabilities. • Model assets, applications, users, service accounts, privileges, vulnerabilities, misconfigurations, controls, alerts, incidents, indicators, threat actors, campaigns, tactics, techniques, and procedures. • Implement graph analytics for attack paths, blast radius, privilege escalation, lateral movement, toxic combinations, identity exposure, control gaps, and vulnerability prioritization. • Build and evaluate graph algorithms and ML models including centrality, community detection, similarity, anomaly detection, node classification, link prediction, embeddings, and Graph Neural Networks. • Design GraphRAG and knowledge-grounded security assistants that combine graph traversal, vector retrieval, structured evidence, LLM reasoning, citations, and human approval controls. • Partner with SOC, threat intelligence, IAM, vulnerability management, cloud security, architecture, data engineering, and product teams to convert operational problems into reusable graph-powered capabilities. • Own technical design reviews, coding standards, model validation, observability, performance tuning, security controls, documentation, and production-readiness gates. • Mentor data scientists and engineers while remaining accountable for prototypes, reference implementations, critical code, troubleshooting, and complex customer or stakeholder demonstrations. Mandatory Hands-on Technical Skills

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Knowledge graphs:

Ontology and semantic model design; property graphs and RDF; graph schema evolution; knowledge representation; provenance; graph quality; entity and relationship resolution. •

Graph platforms:

Deep implementation experience with Neo4j and Cypher; working knowledge of at least one additional platform such as Amazon Neptune, TigerGraph, Azure Cosmos DB Gremlin, ArangoDB, or JanusGraph. •

Graph data science:

Neo4j Graph Data Science, NetworkX, PyTorch Geometric or DGL; graph embeddings, pathfinding, similarity, clustering, link prediction, node classification, anomaly detection, and GNN development. •

Programming and engineering:

Advanced Python and SQL; APIs; test automation; data structures; distributed processing; Git; CI/CD; containers; infrastructure awareness; production debugging and performance optimization. •

Data engineering:

Spark or Databricks, Kafka or equivalent streaming, ETL/ELT, batch and real-time pipelines, data contracts, lineage, cataloguing, quality rules, and scalable cloud storage. •

Cybersecurity:

SOC workflows, threat hunting, incident response, detection engineering, vulnerability and exposure management, IAM/PAM, Zero Trust, cloud security, and security control mapping. •

Security standards:

Practical use of MITRE ATT&CK, STIX/TAXII, CVE, CWE, CAPEC, NIST frameworks, CIS Controls, and common threat-intelligence vocabularies. •

GenAI and GraphRAG:

LLM-based extraction, retrieval orchestration, agent/tool integration, prompt design, evaluation, grounding, guardrails, explainability, and evidence traceability. •

MLOps and observability:

Experiment tracking, model versioning, deployment, monitoring, drift and quality checks, auditability, access controls, secrets management, and cost/performance management. Security Knowledge Graph Engineering Expectations

• Create canonical entity and relationship definitions with stable identifiers, temporal context, source lineage, evidence attributes, confidence scores, and access-control classifications. • Develop reusable connectors and parsers for structured, semi-structured, and unstructured security sources; implement incremental loading, event-time handling, reconciliation, and recovery patterns. • Engineer mappings between enterprise security data and standards such as MITRE ATT&CK and STIX while managing versioning, extensions, and organization-specific concepts. • Design query patterns, graph projections, indexes, constraints, partitioning strategies, retention policies, and caching approaches for high-volume investigative workloads. • Establish graph quality metrics covering completeness, consistency, freshness, uniqueness, referential integrity, semantic accuracy, and unexplained relationship growth. • Implement privacy, least-privilege access, tenant or business-unit segregation, audit logging, encryption, secure development, and responsible AI controls. Illustrative Use Cases

• Attack-path discovery from internet-facing assets to critical applications or privileged identities. • Identity and privilege-risk analysis across users, service accounts, roles, entitlements, devices, applications, and cloud resources. • Threat-intelligence fusion linking indicators, malware, campaigns, threat actors, observed telemetry, vulnerabilities, and affected assets. • Incident investigation timelines, alert correlation, root-cause analysis, blast-radius assessment, and recommended containment evidence. • Risk-based vulnerability prioritization using exploit context, asset criticality, exposure, compensating controls, active threats, and graph proximity. • Graph-grounded security copilots for natural-language investigation, explainable recommendations, detection engineering, and analyst productivity. Leadership & Stakeholder Responsibilities

• Define the technical roadmap, reference architecture, reusable accelerators, engineering backlog, and adoption plan for the Security Knowledge Graph capability. • Facilitate architecture workshops and communicate trade-offs, risks, dependencies, and business value to security leaders, architects, product owners, and engineering teams. • Lead design and code reviews; coach teams on graph modeling, data science rigor, cybersecurity context, and reliable AI engineering. • Coordinate with governance, privacy, legal, risk, and Responsible AI stakeholders to ensure transparent, accountable, and human-supervised use of AI. • Support solution estimation, delivery planning, technical proposals, demonstrations, and executive-level presentations. Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Cybersecurity, Engineering, or a related discipline. • Demonstrated ownership of at least one production knowledge graph or graph analytics solution, preferably in cybersecurity. • Strong portfolio of hands-on Python and graph-query development, data pipeline engineering, testing, and production deployment. • Ability to translate complex security problems into data models, algorithms, measurable experiments, scalable services, and clear technical documentation. • Excellent communication, mentoring, problem-solving, and cross-functional leadership capabilities. Preferred Qualifications

• Experience with cloud-native security and data services on Azure, AWS, or Google Cloud; enterprise graph platforms; modern lakehouse architectures; and security copilots or agentic AI patterns. • Relevant certifications such as CISSP, CISM, GIAC, cloud security, cloud AI/data, or Neo4j Graph Data Science are advantageous but not mandatory. Key Deliverables and Success Measures

• Approved Security Knowledge Graph architecture, ontology, governance model, and phased implementation roadmap. • Production-grade ingestion, entity-resolution, enrichment, graph analytics, API, and GraphRAG components with automated tests and operational documentation. • Measurable improvement in investigation speed, attack-path visibility, correlation quality, vulnerability prioritization, analyst productivity, and explainability. • Reliable service performance demonstrated through data-quality thresholds, query latency and scale targets, monitoring, security reviews, adoption, and stakeholder acceptance. • Sustainable engineering capability through reusable assets, standards, knowledge transfer, mentoring, and a prioritized innovation backlog. Ideal Candidate Profile

A credible technical leader who can move seamlessly from a CISO-level discussion to ontology design, Python development, Cypher optimization, graph algorithm selection, model evaluation, and production troubleshooting. The successful candidate combines cybersecurity depth with strong data-science discipline and builds trustworthy graph-powered solutions that improve real security decisions, not just demonstrations.

TPBN1_UKTJ